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Copy file name to clipboardExpand all lines: README.Rmd
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proActiv is an R package that estimates promoter activity from RNA-Seq data. proActiv uses aligned reads and genome annotations as input, and provides absolute and relative promoter activity as output. The package can be used to identify active promoters and alternative promoters, the details of the method are described at https://doi.org/10.1101/176487.
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proActiv is an R package that estimates promoter activity from RNA-Seq data. proActiv uses aligned reads and genome annotations as input, and provides absolute and relative promoter activity as output. The package can be used to identify active promoters and alternative promoters, the details of the method are described in [Demircioglu et al (2019)](https://www.cell.com/cell/fulltext/S0092-8674(19)30906-7).
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Additional data on differential promoters in tissues and cancers can be downloaded here: https://jglab.org/data-and-software/
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Additional data on differential promoters in tissues and cancers from TCGA, ICGC, GTEx, and PCAWG can be downloaded here: https://jglab.org/data-and-software/
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### Installation
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proActiv can be installed from [GitHub](https://github.com/) with:
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proActiv can be installed from GitHub with:
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```r
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library("devtools")
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devtools::install_github("GoekeLab/proActiv")
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```
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### Annotation and Example Data
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Pre-calculated promoter annotation data for Gencode v19 (GRCh37) is available as part of the proActiv package. The PromoterAnnotation object has 4 slots:
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### Estimate Promoter Activity (after TopHat2 or STAR alignment)
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- reducedExonRanges : The reduced first exon ranges for each promoter with promoter metadata for Gencode v19
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- promoterIdMapping : The id mapping between transcript ids, names, TSS ids, promoter ids and gene ids for Gencode v19
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- annotatedIntronRanges : The intron ranges annotated with the promoter information for Gencode v19
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- promoterCoordinates : Promoter coordinates (TSS) with gene id and internal promoter state for Gencode v19
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Example junction files as produced by TopHat2 and STAR are available as external data. The reference genome used for alignment is Gencode v19 (GRCh37).
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The TopHat2 and STAR example files (5 files each) can be found at 'extdata/tophat2' and 'extdata/star' folders respectively.
This is a basic example to estimate promoter activity from a set of RNA-Seq data which was aligned with TopHat2. proActiv will use the junction file from the TopHat2 alignment (see below for an example with STAR-aligned reads), and a set of annotation objects that describe the associations of promoters, transcripts, and genes, to calculate promoter activity.
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This is a basic example to estimate promoter activity from a set of RNA-Seq data which was aligned with TopHat2 (or STAR). proActiv will use the junction file from the TopHat2 (STAR) alignment, and a set of annotation objects that describe the associations of promoters, transcripts, and genes, to calculate promoter activity.
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```{r, eval = FALSE}
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library(proActiv)
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# Preprocessed data is available as part of the package for the human genome (hg19):
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# Available data: proActiv::promoterAnnotationData.gencode.v19
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### TopHat2 Junction Files Example
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# Preprocessed annotations are available as part of the R package for the human genome (hg19):
Pre-calculated promoter annotation data for Gencode v19 (GRCh37) is available as part of the proActiv package. The PromoterAnnotation object has 4 slots:
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### Estimate Promoter Activity (STAR alignment)
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```{r, eval = FALSE}
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library(proActiv)
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# Preprocessed data is available as part of the package for the human genome (hg19):
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# Available data: proActiv::promoterAnnotationData.gencode.v19
Example junction files as produced by TopHat2 and STAR are available as external data. The reference genome used for alignment is Gencode v19 (GRCh37).
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The TopHat2 and STAR example files (5 files each) can be found at 'extdata/tophat2' and 'extdata/star' folders respectively.
proActiv provides functions to create promoter annotation objects for any genome. Here we describe how the annotation can be created using a TxDb object (please see the TxDb documentation for how to create annotations from a GTF file).
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numberOfCores <- 1
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### Annotation data preparation
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### Needs to be executed once per annotation. Results can be saved and loaded later for reuse
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promoterAnnotationData <- preparePromoterAnnotationData(txdb, species = 'Homo_sapiens', numberOfCores = 1)
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promoterAnnotationData <- preparePromoterAnnotationData(txdb, species = species, numberOfCores = numberOfCores)
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# Retrieve the id mapping between transcripts, TSSs, promoters and genes
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head(promoterIdMapping(promoterAnnotationData))
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## Citing proActiv
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If you use proActiv, please cite:
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Demircioğlu, Deniz, et al. "A Pan-Cancer Transcriptome Analysis Reveals Pervasive Regulation through Tumor-Associated Alternative Promoters." bioRxiv (2018): 176487.
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Demircioğlu, Deniz, et al. "A Pan-cancer Transcriptome Analysis Reveals Pervasive Regulation through Alternative Promoters." *Cell* 178.6 (2019): 1465-1477.
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